scholarly journals Combining Haar Wavelet and Karhunen Loeve Transforms for Medical Images Watermarking

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Mohamed Ali Hajjaji ◽  
El-Bay Bourennane ◽  
Abdessalem Ben Abdelali ◽  
Abdellatif Mtibaa
Keyword(s):  
Fuzzy Inference ◽  
Medical Images ◽  
Haar Wavelet ◽  
Discrete Wavelet ◽  
Correct Identification ◽  
Hidden Information ◽  
Inference System ◽  
Optimal Value ◽  
Karhunen Loeve Transform ◽  
Set Up

This paper presents a novel watermarking method, applied to the medical imaging domain, used to embed the patient’s data into the corresponding image or set of images used for the diagnosis. The main objective behind the proposed technique is to perform the watermarking of the medical images in such a way that the three main attributes of the hidden information (i.e., imperceptibility, robustness, and integration rate) can be jointly ameliorated as much as possible. These attributes determine the effectiveness of the watermark, resistance to external attacks, and increase the integration rate. In order to improve the robustness, a combination of the characteristics of Discrete Wavelet and Karhunen Loeve Transforms is proposed. The Karhunen Loeve Transform is applied on the subblocks (sized8×8) of the different wavelet coefficients (in the HL2, LH2, and HH2 subbands). In this manner, the watermark will be adapted according to the energy values of each of the Karhunen Loeve components, with the aim of ensuring a better watermark extraction under various types of attacks. For the correct identification of inserted data, the use of an Errors Correcting Code (ECC) mechanism is required for the check and, if possible, the correction of errors introduced into the inserted data. Concerning the enhancement of the imperceptibility factor, the main goal is to determine the optimal value of the visibility factor, which depends on several parameters of the DWT and the KLT transforms. As a first step, a Fuzzy Inference System (FIS) has been set up and then applied to determine an initial visibility factor value. Several features extracted from the Cooccurrence matrix are used as an input to the FIS and used to determine an initial visibility factor for each block; these values are subsequently reweighted in function of the eigenvalues extracted from each subblock. Regarding the integration rate, the previous works insert one bit per coefficient. In our proposal, the integration of the data to be hidden is 3 bits per coefficient so that we increase the integration rate by a factor of magnitude 3.

scholarly journals Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction

2021 ◽  
Vol 11 (19) ◽  
pp. 9115
Author(s):  
Menshawy A. Mohamed ◽  
Mohamed A. Moustafa Hassan ◽  
Fahad Albalawi ◽  
Sherif S. M. Ghoneim ◽  
Ziad M. Ali ◽  
...  
Keyword(s):  
Induction Motor ◽  
Fuzzy Inference ◽  
Short Circuit ◽  
Experimental Tests ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Inference System ◽  
Stator Current ◽  
Set Up ◽  
Cage Induction Motor

This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.

Speech Noise Cancellation Based on a Neuro-Fuzzy System: Further Improvements

2001 ◽  
Vol 5 (1) ◽  
pp. 15-21
Author(s):  
Anna Esposito ◽  
◽  
Eugene C. Ezin ◽  
Carlos A. Reyes-Garcia ◽  
◽  
...  
Keyword(s):  
Nonlinear Distortion ◽  
Fuzzy Inference ◽  
Experimental System ◽  
Computational Time ◽  
Further Training ◽  
Inference System ◽  
Car Traffic ◽  
Neuro Fuzzy ◽  
Average Improvement ◽  
Set Up

This work reports on an experimental system based upon the Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture, which is employed for identifying a nonlinear model of the unknown dynamic characteristics of the noise transmission paths. The output of this model is used to subtract the noisy components from the received signal. The novelty of the system described in the present paper, with respect to our previous work, consists in a different set up, which requires more fuzzy rules, generated by seven trapezoidal membership functions, and uses a second order it sinc function to generate the nonlinear distortion of the noise. Once trained for few epochs (only three) with a long sentence corrupted with babble noise, the FIS obtained, has the ability to clean speech sentences corrupted by babble and also by car, traffic, and white noise, in a computational time almost close to realtime. The average improvement, in terms of SNR, was 37 dB without further training.

scholarly journals Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge

2020 ◽  
Vol 20 (8) ◽  
pp. 3156-3171
Author(s):  
Hiwa Farajpanah ◽  
Morteza Lotfirad ◽  
Arash Adib ◽  
Hassan Esmaeili-Gisavandani ◽  
Özgur Kisi ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Topsis Method ◽  
M5 Model Tree ◽  
Inference System ◽  
Flow Discharge ◽  
Discharge Temperature

Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precipitation during 1991 to 2018. The derived results show that the best method is the hybrid of the M5T model with sym7 wavelet function. The MWFs were decomposed by discrete wavelet transform (DWT). The combination of AI models and MWFs improves the correlation coefficient of MLP, RBF, LSSVM, ANFIS, GP, GEP, M5T and BN by 8.05%, 4.6%, 8.14%, 8.14%, 22.97%, 7.5%, 5.75% and 10% respectively.

A Content-Based Approach to Medical Images Retrieval

2013 ◽  
Vol 8 (2) ◽  
pp. 15-27
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Content Based Image Retrieval ◽  
Inference System ◽  
Medical Decision Support ◽  
Neuro Fuzzy

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.

scholarly journals Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 93-104 ◽  
Author(s):  
Theerasak Patcharoen ◽  
Suntiti Yoomak ◽  
Atthapol Ngaopitakkul ◽  
Chaichan Pothisarn
Keyword(s):  
Artificial Intelligence ◽  
Simulation Study ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Capacitor Bank ◽  
Experimental Results ◽  
Discrete Wavelet ◽  
Inrush Current ◽  
Discrete Wavelet Transforms ◽  
Inference System

Abstract This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

Forecasting EGR Rate of Diesel Engine Based on Neural Syncretic Theory

2011 ◽  
Vol 301-303 ◽  
pp. 1789-1794
Author(s):  
Shi Bin Yang ◽  
Ming Jiang Hu
Keyword(s):  
Diesel Engine ◽  
Fuzzy Inference ◽  
Resolving Power ◽  
Emission Model ◽  
Inference System ◽  
Rbf Network ◽  
On Line ◽  
Set Up ◽  
Engine Emission ◽  
Diesel Engine Emission

To counter the influencing emission of the diesel engine by the EGR rate, the emission model of the diesel engine was set up by combining Radial Basis Function neural network with Adaptive Neural Fuzzy Inference System. The model first draws on the nonlinear approaching capacity of the RBF network to forecast the diesel engine emission which takes no account of the factor of the EGR rate, and then, based on influencing the diesel engine emission by the EGR rate, the ANFIS system was used to modify the results of the diesel engine emission obtained by using the RBF network so as to acquire the EGR rate curve. The result showed that the emission model of the diesel engine was reasonable; the forecasting strategy had the good resolving power and could be much fitted for the on-line aging forecast of the EGR rate.

Combining Haar Wavelet and Karhunen-Loeve Transform for Robust and Imperceptible Data Hiding Using Digital Images

2018 ◽  
Vol 27 (1) ◽  
pp. 91-103 ◽  
Author(s):  
Akankasha Sharma ◽  
Amit Kumar Singh ◽  
Pardeep Kumar
Keyword(s):  
Image Processing ◽  
Digital Images ◽  
Image Watermarking ◽  
Haar Wavelet ◽  
Gain Factor ◽  
Performance Comparison ◽  
Discrete Wavelet ◽  
Potential Applications ◽  
Comprehensive Survey ◽  
Karhunen Loeve Transform

Abstract In this paper, we present an introduction of digital image watermarking followed by important characteristics and potential applications of digital watermarks. Further, recent state-of-the-art watermarking techniques as reported by noted authors are discussed in brief. It includes the performance comparison of reported transform/spatial domain based watermarking techniques presented in tabular form. This comprehensive survey will be significant for researchers who will be able to implement more efficient watermarking techniques. Moreover, we present a robust watermarking technique using fusion of discrete wavelet transform (DWT) and Karhunen-Loeve transform for digital images. Further, visual quality of the watermarked image is enhanced by using different image de-noising techniques. The results are obtained by varying the gain factor, size of the image watermark, different DWT sub-bands, and image processing attacks. Experimental results demonstrate that the method is imperceptible and robust for different image processing attacks.

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